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US 2026/0252543 A1Published application
System
AssigneeSoftBank Group Corp.
InventorTsutomu IKUMI
Life of this patent
Estimating the term…
Abstract
The system according to the embodiment comprises a learning unit, an extraction unit, an addition unit, and a generation unit. The learning unit learns from past project plans. The extraction unit extracts content that can be standardized based on the content learned by the learning unit. The addition unit adds specific content based on the standardized content extracted by the extraction unit. The generation unit generates a draft version of a project plan by combining the specific content added by the addition unit with the standardized content.
Claims20
A system comprising:
- circuitry configured to:
- receive, via a communication interface coupled to a packet-switched network, a plurality of structured dataset records from a client terminal;
- apply a data generation model comprising a Transformer-based architecture to the plurality of structured dataset records to extract feature vectors representing reusable patterns within the plurality of structured dataset records;
- generate template data structures based on the extracted feature vectors by applying at least one of clustering analysis or principal component analysis to identify common elements across the plurality of structured dataset records;
- receive, via the communication interface, input parameter data from the client terminal;
- merge the input parameter data into the template data structures by vectorizing the input parameter data using the Transformer-based architecture and evaluating relevance scores between the input parameter data and the template data structures;
- generate output data by combining the template data structures with the merged input parameter data using the data generation model;
- and
- transmit the output data to the client terminal via the communication interface.
6 more elements
A system comprising:
- circuitry configured to:
- receive, via a communication interface coupled to a packet-switched network, a plurality of structured dataset records from a client terminal, each structured dataset record comprising a text portion and a performance indicator portion;
- convert the text portion of each structured dataset record into a context vector having a dimensionality of at least 768 using a Transformer-based encoder of a data generation model, and normalize the performance indicator portion into a numerical vector;
- extract feature vectors from the context vectors by applying at least one of supervised learning comprising a multilayer perceptron, self-supervised learning comprising contrastive learning, or reinforcement learning for template optimization;
- generate template data structures by applying clustering analysis to the extracted feature vectors to identify common elements, and store the template data structures in a database;
- receive, via the communication interface, input parameter data from the client terminal;
- vectorize the input parameter data using a natural language processing encoder of the data generation model, evaluate relevance scores between the vectorized input parameter data and the template data structures using cosine similarity, classify the input parameter data into at least functional requirement data and non-functional requirement data, and merge the classified input parameter data into corresponding sections of the template data structures;
- generate output data by combining the template data structures with the merged input parameter data using the data generation model, the output data comprising at least a structured text document;
- and
- transmit the output data to the client terminal via the communication interface.
7 more elements
A method performed by circuitry of a system, the method comprising:
- receiving, via a communication interface coupled to a packet-switched network, a plurality of structured dataset records from a client terminal;
- applying a data generation model comprising a Transformer-based architecture to the plurality of structured dataset records to extract feature vectors representing reusable patterns within the plurality of structured dataset records;
- generating template data structures based on the extracted feature vectors by applying at least one of clustering analysis or principal component analysis to identify common elements across the plurality of structured dataset records;
- receiving, via the communication interface, input parameter data from the client terminal;
- merging the input parameter data into the template data structures by vectorizing the input parameter data using the Transformer-based architecture and evaluating relevance scores between the input parameter data and the template data structures;
- generating output data by combining the template data structures with the merged input parameter data using the data generation model;
- and
- transmitting the output data to the client terminal via the communication interface.
5 more elements
Dependencies are inferred from the claim text.